People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users’ behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users’ behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user’s location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.

The routing mechanisms in flying ad-hoc networks (FANETs) using unmanned aerial vehicles (UAVs) have been a challenging issue for many reasons, such as its high speed and different directions of use. In FANETs, the routing protocols send hello messages periodically for the maintenance of routes. However, the hello messages that are sent in the network increase the bandwidth wastage on some occasions and the excessive number of hello messages can also cause the problem of energy loss. Scarce works deal with the problem of excessive hello messages in dynamic UAVs scenarios, and treat several other problems, such as bandwidth and energy wastage simultaneously. Generally, the existing solutions configure the hello interval to an excessive long or short time period originating delay in neighbors discovery. Thus, a self-acting approach is necessary for calculating the exact number of hello messages with the aim to reduce the bandwidth wastage of the network and the energy loss; this approach needs to be low complex in terms of computational resource consumption. In order to solve this problem, an intelligent Hello dissemination model, AI-Hello, based on reinforcement learning algorithms, that adapts the hello message interval scheme is proposed to produce a dense reward structure, and facilitating the network learning. Experimental results, considering FANET dynamic scenarios of high speed range with 40 UAVs, show that the proposed method implemented in two widely adopted routing protocols (AODV and OLSR) saved 30.86% and 27.57% of the energy consumption in comparison to the original AODV and OLSR protocols, respectively. Furthermore, our proposal reached better network performance results in relation to the state-of-the-art methods that are implemented in the same protocols, considering parameters, such as routing overhead, packet delivery ratio, throughput and delay.

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems.

This scientific article presents an innovative approach to optimizing routes of high-altitude unmanned aerial vehicles (UAVs) for effective monitoring of smart cities. The main proposed method is based on the ant colony optimization (ACO) algorithm with the implementation of an inverse pheromone mechanism—a repulsion-based exploration strategy. Unlike standard pheromones that encourage exploitation of frequently visited paths, this approach promotes exploration of unvisited areas by repelling UAVs from undesirable route sections, allowing UAVs to adapt more efficiently to dynamic changes in the urban environment. The authors developed a simulation system in the Webots environment, taking into account numerous factors: atmospheric conditions at high altitudes, potential for improved energy efficiency, urban development features, and priority of observation zones. Simulation results demonstrate that the proposed algorithm using inverse pheromones provides more effective area coverage compared to traditional route-planning methods, which may contribute to reduced UAV energy consumption and optimizing the monitoring process in real time. The research makes a significant contribution to the development of smart city technologies, offering a solution that can be integrated with existing urban monitoring systems to improve the efficiency of urban infrastructure observation, enhance security, and optimize urban resource management.

Hybrid digital and analog beamforming are today widely research topics in massive MIMO systems due to low cost and low power consumption as compared to conventional fully digital beamforming method. The beamforming matrix consists of analog radio frequency (RF) precoder and it is implemented by using phase shifters and baseband digital precoder. Carrier aggregation (CA) is a technology that allows bandwidth expansion to improve data capacity by combining two or more carriers in the same or different frequency bands. The digital baseband precoder is the main challenge and can be adapted to the channel of each carrier in the frequency domain while the same analog RF precoder must be applied to all carriers simultaneously in the time domain. The main goal of our research is to combine CA technology with hybrid digital and analog beamforming architecture to provide an improved system performance with less penalty. This paper proposes a hybrid precoding algorithm to support two carriers aggregated that are based on this scenario. We present numerical results that prove theoretical expressions and give practical explanation of the proposed method demonstrating its superiority when compared to the traditional hybrid beamforming design.

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications.